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Article published In:
Translation Spaces
Vol. 11:2 (2022) ► pp.184–212

Creativity in translation

Machine translation as a constraint for literary texts

ORCID logo | University of Surrey | University of Groningen
ORCID logo | University of Groningen
This article presents the results of a study involving the translation of a short story by Kurt Vonnegut from English to Catalan and Dutch using three modalities: machine-translation (MT), post-editing (PE) and translation without aid (HT). Our aim is to explore creativity, understood to involve novelty and acceptability, from a quantitative perspective. The results show that HT has the highest creativity score, followed by PE, and lastly, MT, and this is unanimous from all reviewers. A neural MT system trained on literary data does not currently have the necessary capabilities for a creative translation; it renders literal solutions to translation problems. More importantly, using MT to post-edit raw output constrains the creativity of translators, resulting in a poorer translation often not fit for publication, according to experts.
Keywords: creativity, machine translation post-editing, literary translation, creative shift, acceptability
Article outline
  • Introduction
  • Related Work
  • Research methodology
    • Source text
    • Machine translation
    • Translation process
    • Translators
    • Review process
    • Measuring creativity
      • Acceptability
      • Units of creative potential (UCP)
      • Novelty
      • Creativity scoring
    • Interviews
  • Results
    • Results on translators’ changes and effort
    • HTER
    • Translation effort according to PET
    • Results from the review
      • Post-task questionnaire
      • Acceptability
      • Creative Shifts
      • Creativity Score
    • Interviews with translators and reviewers
      • 1.MT can only be partially useful in literary translation
      • 2.Creativity is a problem-solving process
      • 3.The proposals act as a constraint to creativity
      • 4.The delicate equilibrium of reviewing
      • 5.The ideal tool for literary translation is human centred
      • 6.The technology or modality impact depends on the type of reader
      • 7.The final translation as a product of many collaborative steps
      • 8.The grim future of translation and technology
  • Conclusions
  • Acknowledgements
  • Notes
  • References
Published online: 28 March 2022
DOI logo
https://doi.org/10.1075/ts.21025.gue
References (49)
References
Aziz, Wilker, Sheila Castilho, and Lucia Specia. 2012. ‘PET: A Tool for Post-Editing and Assessing Machine Translation’. LREC, 3982–87.Google Scholar
Bayer-Hohenwarter, Gerrit. 2009. ‘Translational Creativity: How to Measure the Unmeasurable’. In Behind the Mind: Methods, Models and Results in Translation Process Research, edited by Susanne Göpferich, Arnt Lykke Jakobsen, and Inger M. Mees, 371:39–59. Copenhagen: Samfundslitteratur.Google Scholar
. 2010. ‘Comparing Translational Creativity Scores of Students and Professionals: Flexible Problem-Solving and/or Fluent Routine Behaviour’. In New Approaches in Translation Process Research, edited by Susanne Göpferich, Fabio Alves, and Inger M. Mees, 831:83–113. 391. Copenhagen: Samfundslitteratur.Google Scholar
. 2011. ‘Creative Shifts as a Means of Measuring and Promoting Translational Creativity’. Meta 56 (3): 663–92. DOI logoGoogle Scholar
. 2012. Translatorische Kreativität: Definition – Messung – Entwicklung. Translations-Wissenschaft. Narr. [URL]
. 2013. ‘Triangulating Translational Creativity Scores’. In Tracks and Treks in Translation Studies: Selected Papers from the EST Congress, Leuven 2010, edited by Catherine Way, Sonia Vandepitte, Reine Meylaerts, and Magdalena Bartłomiejczyk, 1081:63–85. New York, Amsterdam: Benjamins. [URL]. DOI logo
Bayer-Hohenwarter, Gerrit, and Paul Kussmaul. 2020. ‘Translation, Creativity and Cognition’. In The Routledge Handbook of Translation and Cognition, edited by Fabio Alves and Arnt Lykke Jakobsen, 1st ed., 310–25. Routledge. DOI logoGoogle Scholar
Busselle, Rick, and Helena Bilandzic. 2009. ‘Measuring Narrative Engagement’. Media Psychology 12 (4): 321–47. DOI logoGoogle Scholar
Daems, Joke. 2021. ‘Wat denken literaire vertalers echt over technologie?’ Tijdschrift over vertalen Filter, 24 January 2021.Google Scholar
Fontanet, Mathilde. 2005. ‘Temps de créativité en traduction’. Meta 50 (2): 432–47. DOI logoGoogle Scholar
Fonteyne, Margot, Arda Tezcan, and Lieve Macken. 2020. ‘Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level’. In Proceedings of the 12th Language Resources and Evaluation Conference, 3790–98. Marseille, France: European Language Resources Association. [URL]
Guerberof-Arenas, Ana, and Antonio Toral. 2020. ‘The Impact of Post-Editing and Machine Translation on Creativity and Reading Experience’. Translation Spaces. John Benjamins. DOI logoGoogle Scholar
Guilford, Joy Paul. 1950. ‘Creativity’. American Psychologist, no. 5: 444–54. DOI logoGoogle Scholar
Hakemulder, Jemeljan F. 2004. ‘Foregrounding and Its Effect on Readers’ Perception’. Discourse Processes 38 (2): 193–218. DOI logoGoogle Scholar
Heiden, Tanja. 2005. ‘Blick in die Black Box: Kreative Momente im Übersetzungsprozess: eine experimentelle Studie mit Translog’. Meta 50 (2): 448–72. DOI logoGoogle Scholar
Kenny, Dorothy. 2006. ‘Creativity in Translation: Opening up the Corpus-Based Approach’. In Proceedings of the Conference Held on 12th November 2005 in Portsmouth, 72–81. Portsmouth: University of Portsmouth.Google Scholar
Kenny, Dorothy, and Marion Winters. 2020. ‘Machine Translation, Ethics and the Literary Translator’s Voice’. Translation Spaces, Fair MT: Towards ethical, sustainable Machine Translation, 9 (1): 123–49. DOI logoGoogle Scholar
Kolb, Waltraud. 2021. ‘Literary Translation and Post-Editing: Priming Effects and Engagement with the Text’. In Computer-Assisted Literary Translation Conference CALT2021@Swansea, 231. [URL]
Koponen, Maarit, Umut Sulubacak, Kaisa Vitikainen, and Jörg Tiedemann. 2020. ‘MT for Subtitling: User Evaluation of Post-Editing Productivity’. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 115–24. Lisboa, Portugal: European Association for Machine Translation. [URL]
Kussmaul, Paul. 1991. ‘Creativity in the Translation Process: Empirical Approaches’. In Translation Studies: The State of the Art. Proceedings from the First James S. Holmes Symposium on Translation Studies, edited by Kitty M. van Leuven-Zwart and Ton Naaijkens, 91–101. Issue 9 of Approaches to Translation Studies. Amsterdam: Rodopi.Google Scholar
. 1995. ‘Creativity in Translation’. In Training the Translator. Benjamins Translation Library, v. 10. Amsterdam: John Benjamins Publishing. DOI logoGoogle Scholar
Lacruz, Isabel, Michael Denkowski, and Alon Lavie. 2014. ‘Cognitive Demand and Cognitive Effort in Post-Editing’. In Proceedings of the 11th Conference of the Association For Machine Translation in the Americas, Workshop on Post-Editing Technology and Practice (WPTP-3), 73–84. Vancouver, BC, Canada.Google Scholar
Lee, Katherine, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018. ‘Hallucinations in Neural Machine Translation’, September. [URL]
Malmkjær, Kirsten. 2019. Translation and Creativity. London and New York: Routledge: Taylor & Francis Group. DOI logoGoogle Scholar
Mohar, Tjaša, Sara Orthaber, and Tomaž Onič. 2020. ‘Machine Translated Atwood: Utopia or Dystopia?’ ELOPE: English Language Overseas Perspectives and Enquiries 17 (1): 125–41. DOI logoGoogle Scholar
Moorkens, Joss, Antonio Toral, Sheila Castilho, and Andy Way. 2018. ‘Translators’ Perceptions of Literary Post-Editing Using Statistical and Neural Machine Translation’. Translation Spaces 7 (2): 240–62. DOI logoGoogle Scholar
Nuland, Sherwin B. 1995. How We Die: Reflections of Life’s Final Chapter, New Edition. 1st edition. New York: Vintage.Google Scholar
O’Sullivan, Carol. 2013. ‘Creativity’. In Handbook of Translation Studies, edited by Yves Gambier and Luc van Doorslaer, 41:42–46. Amsterdam: John Benjamins Publishing Company. DOI logoGoogle Scholar
Rojo, Ana. 2017. ‘The Role of Creativity’. In The Handbook of Translation and Cognition, edited by John W. Schwieter and Aline Ferreira, 1st ed., 350–68. Wiley. DOI logoGoogle Scholar
Rojo, Ana, and María Ramos Caro. 2016. ‘Can Emotion Stir Translation Skill? Defining the Impact of Positive and Negative Emotions on Translation Performance’. In Reembedding Translation Process Research, edited by Ricardo Muñoz, 1281:107–30. Benjamins Translation Library. Amsterdam: John Benjamins Publishing. DOI logoGoogle Scholar
Rudowicz, Elisabeth. 2003. ‘Creativity and Culture: A Two Way Interaction’. Scandinavian Journal of Educational Research 47 (3): 273–90. DOI logoGoogle Scholar
Ruffo, Paola. 2021. ‘In-between Role and Technology: Literary Translators on Navigating the New Socio-Technological Paradigm’. Edinburgh: Heriot-Watt University.Google Scholar
Runco, Mark A. 2014. Creativity. Second edition. Academic Press.Google Scholar
Runco, Mark A., and Jarret J. Jaeger. 2012. ‘The Standard Definition of Creativity’. Creativity Research Journal 24 (1): 92–96. DOI logoGoogle Scholar
Şahin, Mehmet, and Sabri Gürses. 2019. ‘Would MT Kill Creativity in Literary Retranslation?’ In Proceedings of the Qualities of Literary Machine Translation, 26–34. Dublin, Ireland: European Association for Machine Translation. [URL]
Sautoy, Marcus Du. 2019. The Creativity Code: How AI Is Learning to Write, Paint and Think. 4th Estate. DOI logoGoogle Scholar
Sawyer, R. Keith. 2006. Explaining Creativity: The Science of Human Innovation. Oxford ; New York: Oxford University Press.Google Scholar
Schmidtke, Dag, and Declan Groves. 2019. ‘Automatic Translation for Software with Safe Velocity’. In Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks, 159–66. Dublin: European Association for Machine Translation. [URL]
Slessor, Stephen. 2020. ‘Tenacious Technophobes or Nascent Technophiles? A Survey of the Technological Practices and Needs of Literary Translators’. Perspectives 28 (2): 238–52. DOI logoGoogle Scholar
Snover, Matthew, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. ‘A Study of Translation Edit Rate with Targeted Human Annotation’. Proceedings of Association for Machine Translation in the Americas 2001: 223–31.Google Scholar
Tezcan, Arda, Joke Daems, and Lieve Macken. 2019. ‘When a “Sport” Is a Person and Other Issues for NMT of Novels’. In, 111.Google Scholar
Toral, Antonio, Andreas van Cranenburgh, and Tia Nutters. 2021. ‘Literary-Adapted Machine Translation in a Well-Resourced Language Pair’. In Proceedings of the 7th Conference of The International Association for Translation and Inter- Cultural Studies (IATIS). Barcelona.Google Scholar
Toral, Antonio, Antoni Oliver, and Pau Ribas-Bellestín. 2020. ‘Machine Translation of Novels in the Age of Transformer’. In Maschinelle Übersetzung für Übersetzungsprofis, edited by Jörg Porsiel, 276–96. Berlin: BDÜ Fachverlag.Google Scholar
Toral, Antonio, Martijn Wieling, and Andy Way. 2018. ‘Post-Editing Effort of a Novel With Statistical and Neural Machine Translation’. Frontiers in Digital Humanities 51: 1–11. DOI logoGoogle Scholar
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. ‘Attention Is All You Need’. In Advances in Neural Information Processing Systems 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 5998–6008. Curran Associates, Inc. [URL]
Vieira, Lucas, Xiaochun Zhang, Roy Yodaule, and Michael Carl. 2020. ‘Machine Translation and Literary Texts: A Network of Possibilities’. Presented at the Creative Translation and Technologies Expert Meeting, University of Surrey, May 29. [URL]
Vonnegut, Kurt. 1999. Bagombo Snuff Box. United States: G. P. Putnam’s Sons.Google Scholar
Webster, Rebecca, Margot Fonteyne, Arda Tezcan, Lieve Macken, and Joke Daems. 2020. ‘Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics’. Informatics 7 (3): 32. DOI logoGoogle Scholar
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